Book Image

Microsoft SQL Server 2012 with Hadoop

By : Debarchan Sarkar
Book Image

Microsoft SQL Server 2012 with Hadoop

By: Debarchan Sarkar

Overview of this book

With the explosion of data, the open source Apache Hadoop ecosystem is gaining traction, thanks to its huge ecosystem that has arisen around the core functionalities of its distributed file system (HDFS) and Map Reduce. As of today, being able to have SQL Server talking to Hadoop has become increasingly important because the two are indeed complementary. While petabytes of unstructured data can be stored in Hadoop taking hours to be queried, terabytes of structured data can be stored in SQL Server 2012 and queried in seconds. This leads to the need to transfer and integrate data between Hadoop and SQL Server. Microsoft SQL Server 2012 with Hadoop is aimed at SQL Server developers. It will quickly show you how to get Hadoop activated on SQL Server 2012 (it ships with this version). Once this is done, the book will focus on how to manage big data with Hadoop and use Hadoop Hive to query the data. It will also cover topics such as using in-memory functions by SQL Server and using tools for BI with big data. Microsoft SQL Server 2012 with Hadoop focuses on data integration techniques between relational (SQL Server 2012) and non-relational (Hadoop) worlds. It will walk you through different tools for the bi-directional movement of data with practical examples. You will learn to use open source connectors like SQOOP to import and export data between SQL Server 2012 and Hadoop, and to work with leading in-memory BI tools to create ETL solutions using the Hive ODBC driver for developing your data movement projects. Finally, this book will give you a glimpse of the present day self-service BI tools such as Excel and PowerView to consume Hadoop data and provide powerful insights on the data.
Table of Contents (12 chapters)

Chapter 3. Using the Hive ODBC Driver

Hive is a framework that sits on top of core Hadoop. It acts as a data warehousing system on top of HDFS and provides easy query mechanisms to the underlying HDFS data. Programming MapReduce jobs could be tedious and will have their own development, testing, and maintenance investments. Hive queries, called Hive Query Language (HQL) are broken down into MapReduce jobs under the hood and remain a complete abstraction to the user and provide a query-based access mechanism for Hadoop data. The simplicity and "SQL" – ness of the Hive queries have made it a popular and preferred choice for users, particularly, people familiar with traditional SQL skills love it since the ramp up time is much less. The following figure gives an overview of the Hive architecture:

In effect, Hive enables you to create an interface layer over MapReduce that can be used in a similar fashion to a traditional relational database; enabling business users to use familiar tools such...